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@article{180483, author = {Prof. Shah Saloni Niranjan and Mr. Pawar R.B. and Ms. Raut A.S. and Ms. Jadhav S.N.}, title = {Diagnosing Respiratory Conditions Via Lung Sounds using CNN-LSTM}, journal = {International Journal of Innovative Research in Technology}, year = {2025}, volume = {12}, number = {1}, pages = {1337-1341}, issn = {2349-6002}, url = {https://ijirt.org/article?manuscript=180483}, abstract = {Respiratory diseases rank among the foremost causes of mortality globally. While traditional lung auscultation is effective, it is hindered by limitations such as interference from background noise and reliance on the expertise of healthcare professionals. Recently, machine learning has emerged as a promising approach for the automated analysis of lung sounds, enhancing diagnostic accuracy and reducing the time required for diagnosis. This study is dedicated to the development of an automated system for lung sound classification, utilizing GTCC-based features in conjunction with a Multi-Layer Perceptron (MLP) classifier. Our system, trained on a comprehensive dataset comprising over 6,800 audio clips, achieved an impressive classification accuracy of 99.22%, underscoring its potential to facilitate the early detection of respiratory diseases.}, keywords = {Machine Learning, Lung Sound Analysis, GTCC Features, Deep Learning, Respiratory Diseases.}, month = {June}, }
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